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TensorLayer is a deep learning and reinforcement learning library based on TensorFlow. It provides rich data processing, model training and serving modules to help both researchers and engineers build practical machine learning workflows.



TensorLayer has install prerequisites including TensorFlow, numpy and matplotlib. For GPU support, CUDA and cuDNN are required. Please check documentation for detailed instructions.

If you already had the pre-requisites ready (numpy, scipy, scikit-image, matplotlib and nltk(optional)), the simplest way to install TensorLayer in your python program is:

[for master version] pip install git+ (Highly Recommended)
[for stable version] pip install tensorlayer


Examples can be found in this folder and Github topic.


Computer Vision

Natural Language Processing

Adversarial Learning

Reinforcement Learning

Special Examples


Design Principles

As deep learning practitioners, we have been looking for a library that can serve for various development phases. This library shall be easy for beginners by providing rich neural network reference implementations. Later, it can be extended to address real-world problems by controlling training backends to exhibit low-level cognitive behaviours. In the end, it shall be able to serve in challenging production environments.

TensorLayer is designed for beginning, intermediate and professional deep learning users with following goals:

  • Simplicity : TensorLayer lifts the low-level dataflow abstraction of TensorFlow to high-level deep learning modules. A user often find it easy to bootstrap with TensorLayer, and then dive into low-level implementation only if need.
  • Transparency : TensorLayer provides access to the native APIs of TensorFlow. This helps users achieve flexible controls within the training engine.
  • Composability : If possible, deep learning modules are composed, not built. TensorLayer can glue existing pieces together (e.g., connected with TF-Slim and Keras).
  • Performance : TensorLayer provides zero-cost abstraction (see Benchmark below). It can run on distributed and heterogeneous TensorFlow platforms with full power.


A common concern towards TensorLayer is performance overhead. We investigate this by running classic models using TensorLayer and native TensorFlow implementations on a Titan X Pascal GPU. The following are the training throughputs of respective tasks:

TensorLayer 2528 images/s 18063 words/s 58167 words/s
TensorFlow 2530 images/s 18075 words/s 58181 words/s

Frequent Questions

A frequent question regarding TensorLayer is what is the different with other libraries like Keras, TFSlim and Tflearn. These libraries are comfortable to start with. They provide imperative abstractions to lower adoption barrier; but in turn mask the underlying engine from users. Though good for bootstrap, it becomes hard to tune and modify from the bottom, which is quite necessary in tackling many real-world problems.

Without compromise in simplicity, TensorLayer advocates a more flexible and composable paradigm: neural network libraries shall be used interchangeably with the native engine. This allows users to enjoy the ease of pre-built modules without losing visibility to the deep. This non-intrusive nature also makes it viable to consolidate with other TF's wrappers.

TensorLayer is in an active development stage and has received numerous contributions from an open community. It has been widely used by researchers from Imperial College London, Carnegie Mellon University, Stanford University, Tsinghua University, UCLA, Linköping University and etc., as well as engineers from Google, Microsoft, Alibaba, Tencent, Penguins Innovate, ReFULE4, Bloomberg and many others.


The documentation [Online] [PDF] [Epub] [HTML] describes the usages of TensorLayer APIs. It is also a self-contained document that walks through different types of deep neural networks, reinforcement learning and their applications in Natural Language Processing (NLP) problems.

We have included the corresponding modularized implementations of Google TensorFlow Deep Learning tutorial, so you can read the TensorFlow tutorial [en] [cn] along with our document. Chinese documentation is also available.


TensorLayer is maintained by numerous Github contributors here.


If you find it is useful, please cite our paper in your project and paper.

author = {Dong, Hao and Supratak, Akara and Mai, Luo and Liu, Fangde and Oehmichen, Axel and Yu, Simiao and Guo, Yike},
journal = {ACM Multimedia},
title = {{TensorLayer: A Versatile Library for Efficient Deep Learning Development}},
url = {},
year = {2017}


TensorLayer is released under the Apache 2.0 license.


A Open Source Deep Learning Library



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